Machine learning algorithms for the prediction of non-metallic inclusions in steel wires for tire reinforcement

Miguel Cuartas, Estela Ruiz, Diego Ferreño (), Jesús Setién, Valentín Arroyo and Federico Gutiérrez-Solana
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Miguel Cuartas: University of Cantabria, E.T.S. de Ingenieros de Caminos, Canales y Puertos
Estela Ruiz: Global Steel Wire, GSW
Diego Ferreño: University of Cantabria, E.T.S. de Ingenieros de Caminos, Canales y Puertos
Jesús Setién: University of Cantabria, E.T.S. de Ingenieros de Caminos, Canales y Puertos
Valentín Arroyo: University of Cantabria, E.T.S. de Ingenieros de Caminos, Canales y Puertos
Federico Gutiérrez-Solana: University of Cantabria, E.T.S. de Ingenieros de Caminos, Canales y Puertos

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 6, No 15, 1739-1751

Abstract: Abstract Non-metallic inclusions are unavoidably produced during steel casting resulting in lower mechanical strength and other detrimental effects. This study was aimed at developing a machine learning algorithm to classify castings of steel for tire reinforcement depending on the number and properties of inclusions, experimentally determined. 855 observations were available for training, validation and testing the algorithms, obtained from the quality control of the steel. 140 parameters are monitored during fabrication, which are the features of the analysis; the output is 1 or 0 depending on whether the casting is rejected or not. The following algorithms have been employed: Logistic Regression, K-Nearest Neighbors, Support Vector Classifier (linear and RBF kernels), Random Forests, AdaBoost, Gradient Boosting and Artificial Neural Networks. The reduced value of the rejection rate implies that classification must be carried out on an imbalanced dataset. Resampling methods and specific scores for imbalanced datasets (recall, precision and AUC rather than accuracy) were used. Random Forest was the most successful algorithm providing an AUC in the test set of 0.85. No significant improvements were detected after resampling. The optimized Random Forest allows the samples with a higher probability of being rejected to be selected, thus improving the effectiveness of the quality control. In addition, the optimized Random Forest has enabled to identify the most important features, which have been satisfactorily interpreted on a metallurgical basis.

Keywords: Machine learning; Steel wire; Continuous casting; Non-metallic inclusions; Random Forest; Imbalanced dataset (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10845-020-01623-9

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